README.md

metadata

license: other
base_model: nvidia/mit-b5
tags:
  - generated_from_trainer
model-index:
  - name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium_16
    results: []

SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium_16

This model is a fine-tuned version of nvidia/mit-b5 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0121
• Mean Iou: 0.9823
• Mean Accuracy: 0.9920
• Overall Accuracy: 0.9954
• Accuracy Background: 0.9974
• Accuracy Melt: 0.9828
• Accuracy Substrate: 0.9958
• Iou Background: 0.9943
• Iou Melt: 0.9594
• Iou Substrate: 0.9932

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Melt	Accuracy Substrate	Iou Background	Iou Melt	Iou Substrate
0.1196	0.7937	50	0.1076	0.8582	0.8965	0.9626	0.9674	0.7265	0.9955	0.9625	0.6696	0.9424
0.2728	1.5873	100	0.0878	0.8762	0.9239	0.9665	0.9622	0.8161	0.9935	0.9611	0.7150	0.9525
0.2668	2.3810	150	0.1131	0.8710	0.9238	0.9639	0.9971	0.8140	0.9602	0.9620	0.7076	0.9432
0.0337	3.1746	200	0.0610	0.9173	0.9613	0.9778	0.9709	0.9208	0.9923	0.9685	0.8110	0.9723
0.0443	3.9683	250	0.0295	0.9527	0.9665	0.9885	0.9924	0.9095	0.9977	0.9902	0.8867	0.9812
0.0283	4.7619	300	0.0220	0.9652	0.9781	0.9915	0.9965	0.9429	0.9950	0.9910	0.9175	0.9871
0.0166	5.5556	350	0.0193	0.9683	0.9837	0.9922	0.9972	0.9609	0.9929	0.9925	0.9249	0.9876
0.0218	6.3492	400	0.0190	0.9691	0.9871	0.9922	0.9975	0.9730	0.9909	0.9919	0.9277	0.9879
0.0178	7.1429	450	0.0157	0.9752	0.9853	0.9938	0.9981	0.9626	0.9951	0.9925	0.9424	0.9909
0.0165	7.9365	500	0.0151	0.9771	0.9878	0.9941	0.9966	0.9711	0.9957	0.9931	0.9470	0.9911
0.0136	8.7302	550	0.0137	0.9785	0.9902	0.9945	0.9955	0.9792	0.9959	0.9930	0.9508	0.9918
0.0127	9.5238	600	0.0128	0.9798	0.9896	0.9948	0.9977	0.9758	0.9955	0.9937	0.9536	0.9923
0.0117	10.3175	650	0.0123	0.9809	0.9895	0.9951	0.9974	0.9747	0.9964	0.9939	0.9561	0.9927
0.011	11.1111	700	0.0125	0.9805	0.9923	0.9950	0.9974	0.9848	0.9946	0.9938	0.9552	0.9925
0.0108	11.9048	750	0.0123	0.9809	0.9915	0.9951	0.9975	0.9818	0.9952	0.9940	0.9561	0.9926
0.0135	12.6984	800	0.0126	0.9808	0.9920	0.9950	0.9979	0.9834	0.9946	0.9941	0.9558	0.9924
0.0089	13.4921	850	0.0123	0.9814	0.9923	0.9952	0.9968	0.9844	0.9957	0.9940	0.9574	0.9929
0.0077	14.2857	900	0.0119	0.9819	0.9911	0.9953	0.9976	0.9797	0.9959	0.9942	0.9586	0.9930
0.0069	15.0794	950	0.0122	0.9822	0.9914	0.9954	0.9973	0.9807	0.9961	0.9943	0.9591	0.9931
0.0069	15.8730	1000	0.0120	0.9822	0.9920	0.9954	0.9975	0.9828	0.9957	0.9944	0.9592	0.9931
0.0089	16.6667	1050	0.0120	0.9824	0.9914	0.9955	0.9976	0.9807	0.9961	0.9943	0.9595	0.9932
0.0072	17.4603	1100	0.0121	0.9823	0.9920	0.9954	0.9974	0.9828	0.9958	0.9943	0.9594	0.9932

Framework versions

• Transformers 4.41.2
• Pytorch 2.0.1+cu117
• Datasets 2.19.2
• Tokenizers 0.19.1